The present invention is of an inspection method for detecting irregularities (hereafter referred to as defects) in two-dimensional periodic structures such as wafer dies or photomasks, and so forth. The invention permits sequential inspection of these periodic structures in real-time, which includes the examination of periods at the edge of the two-dimensional structure with no loss of throughput.
Periodic structures such as semiconductor wafer dies, memory cells, and photomasks require inspection during manufacture in order to detect the presence of defects as they appear thus reduce production costs. Such inspection cannot be performed entirely manually, since manual inspection would be too intensive and would require many hours of human labor. Instead, inspection is performed automatically, by moving the object containing the structure relative to an optical system for inspecting at least a portion of the object. For the sake of clarity it is convenient to model the system as a camera of limited width obtaining sequential images of a portion of the object, in a process known as xe2x80x9cscanningxe2x80x9d, until the entire desired area is scanned.
Each area of the object which is scanned by a single stroke of the camera is a xe2x80x9cswathxe2x80x9d. For wafer dies, the width of the swath is typically less than the width of the die
For wafers, the swath which includes only a single period (single die) of the wafer is defined as a xe2x80x9cdie swathxe2x80x9d. A swath covering the same portion of the die for all dies in the wafer is defined as a xe2x80x9cvirtual swathxe2x80x9d. A virtual swath features the images of a number of die swaths, preferably substantially all die swaths in the wafer, concatenated into a long string of die swath images taken from substantially the same portion of each die in the wafer.
An example of the three swath types is given in background art FIG. 1A. A wafer 10 features a plurality of dies 12 which are organized into rows 14. Each die 12 is shown with a die swath 16 in substantially the same location for all dies 12. A set of die swaths 16 from each row 14 is a swath 18. All swaths 18 together form a virtual swath.
A classical detection process is based on the analysis of matching signals obtained from a number of periods. The detection of defects is based on a statistical approach, meaning the probability that a defect will exist on the same location within adjacent dies is very low. Hence detection is based on locating irregularities through the use of three-die comparison method which is shown in FIG. 1B.
FIG. 1B shows a swath 20 which features five die swaths 22 from five dies, labeled as xe2x80x9cAxe2x80x9d, xe2x80x9cBxe2x80x9d, xe2x80x9cCxe2x80x9d, xe2x80x9cDxe2x80x9d and xe2x80x9cExe2x80x9d. The intensity difference for images of each pair of adjacent die swaths 22 is compared to a threshold value, the output of the comparison being a comparison signal 26. When intensity difference exceeds the threshold value, comparison signal 26 is said to be significant. Therefore, the proper threshold must be set, such that the system is sensitive enough to detect low contrast defects and robust enough to ignore high contrast noise. Hence, threshold values should represent a tight estimator of pixel noise.
In FIG. 1B, comparison signals 26 are labeled as AB, BC, CD and DE. Each comparison signal 26 is an image marking the position of irregularities where there is a significant deviation between the signals obtained for each pair of adjacent die swaths 22. Various algorithms have been proposed for filtering the signals and for determining the proper threshold at which a different signal from one die indicates a potential defect in the die. Examples of such algorithms are disclosed in U.S. Pat. No. 5,537,669.
A defect image 28 is the result of the defect identification procedure performed with a pair of adjacent comparison signals 26. A defect identification procedure marks a defect in a certain die if irregularities appear at the same location in the comparison with its neighboring dies.
Since defects are expected to be both statistically random and relatively infrequent events, any defect is statistically unlikely to appear in the same location on two or three wafer dies. Thus, by performing a defect identification procedure between comparison signals obtained from adjacent dies, the presence of a defect 25 (if any) can be detected and the detection of random noise is reduced.
The term xe2x80x9crandom noisexe2x80x9d refers to noise that is introduced in the intensity comparison procedure. Such comparison typically has an increased variance, thus there is a finite probability that the intensity comparison will exceed the threshold value to produce random noise 24 when a defect is absent. At typical threshold values the probability of such event is small for one comparison and is almost zero for two such events on the same pixel. Hence the defect identification operation should reduce the occurrence of such events to zero or near-zero levels.
A defect in the die labeled as xe2x80x9cBxe2x80x9d yields a significant comparison signal 26 between die swaths 22 labeled as xe2x80x9cAxe2x80x9d and xe2x80x9cBxe2x80x9d as well as between the dies labeled as xe2x80x9cBxe2x80x9d and xe2x80x9cCxe2x80x9d, such that the defect identification operation is true at the position of the defect.
The advantage of this method is that the defect identification operation results in the cancellation of much of the noise since a defect should produce significant comparison signals 26 for two adjacent die swaths 22. In addition, such a process is particularly suitable for a real-time image processing system, since the steps required for image acquisition and processing are well defined and are performed repetitively. These steps are as follows. First, an image of a die swath for die xe2x80x9cAxe2x80x9d, or xe2x80x9cdie swath Axe2x80x9d, is grabbed and stored in the system memory. Next, an image of a die swath for die xe2x80x9cBxe2x80x9d, or xe2x80x9cdie swath Bxe2x80x9d, is grabbed and stored. Each image is grabbed as a plurality of frames, which are processing units within a die swath. Each incoming frame of die swath B is aligned to the corresponding frame of die swath A for comparison, such that a reliable comparison signal is obtained.
As all of the images for die swath B are grabbed, a comparison image is produced which is termed image AB. Next, images for die swath C are grabbed and comparison image BC is generated. The defect identification operation which is performed between images AB and BC permits the defects found in die B to be detected. Unfortunately, this method is not effective for detecting defects at edge dies such as dies A and E. For example, defects at die A may be detected as the result of the defect identification operation between AB and BC. Such defects produce a significant comparison signal at AB while the corresponding portion of the BC signal is free of such irregularities. Hence, the defect identification operation for an edge die, such as die A, is performed only once and is sensitive to the presence of high contrast noise, the thereby giving a misleading result.
Thus, the inspection of edge dies has two difficulties. First, such detection requires additional processing steps, for example in order to perform an additional defect identification operation, which are not included in the typical processing path for the remainder of the wafer, reducing system throughput. In addition, this operation produces a significant number of false positive results for the detection of defects because of the presence of random noise.
These problems of the detection of defects at edge dies are known in the art, and have a number of currently known but deficient solutions. The first solution is simply to ignore all edge dies during the inspection process, declaring all such dies to be unfit. This solution is clearly disadvantageous, since eliminating all edge dies is inefficient and costly. The second solution is to increase the comparison threshold such that significant differences need to be even greater for edge dies. This solution eliminates most of the random noise, but also reduces the detection sensitivity. The third solution is to confirm the presence of defects on edge dies using an additional post-processing phase, in which edge dies are examined with a second comparison with a die which is separated from the edge die by two dies. The disadvantage of such method is the additional time overhead required for such an operation.
Yet another solution is to compare signals of images obtained from two swaths of wafer dies, as shown in FIG. 1C. For this solution, at least two rows of wafer dies 14 are required. A comparison is performed between the signals which are obtained from the images of the first die 12 of each row 14, both of which are edge dies 12, effectively giving each edge die 12 two xe2x80x9cneighborsxe2x80x9d for comparison. However, the compared images have opposite orientations at the edges, such that the orientation of one of the images must be reversed before the comparison can be performed. One disadvantage of this method is that the images from two neighboring swaths 16 are taken at reverse orientations of wafer 10 relative to the camera, often introducing an artifactual deviation in the comparison signals. Such noise sources cause detection sensitivity to be lost in order to avoid false positives for the detection of defects. Thus, a preferred solution would feature the comparison of two or more signals obtained with the same image orientation. Unfortunately, such a solution is not available.
There is thus a need for, and it would be useful to have, a method for detecting defects in a periodic structure, such as a wafer of semiconductor dies, which enables the real-time inspection of edge dies with maximal detection sensitivity and throughput.
The present invention is of a method for comparing periodic structures, such as wafer dies, by obtaining signals from the images of an even number of virtual swaths of wafer dies simultaneously. The signals for each wafer die are then compared to at least the signals from two other dies. Preferably, these two dies are located on either side of the die, in the same row as that die. However, for edge dies, at least one neighbor die is located in another row, preferably an adjacent row and on the opposite side of the swath.
In the method of the present invention, images are grabbed and processed from an even number of swaths, such as two or more swaths, in the first row of dies before images are taken from equivalent swaths in a second row of dies. The camera (and hence the resultant image) is correctly oriented for obtaining high quality signals from the equivalent swaths in the second row of dies. Such a method permits the correct comparison of signals obtained from swaths of the first row of dies and second row of dies, including edge dies.
Hereinafter, the term xe2x80x9cswathxe2x80x9d refers to each area of an object, which is scanned by a single stroke of the camera across the object. Hereinafter, the term xe2x80x9cperiodic structurexe2x80x9d includes but is not limited to semiconductor wafer dies, memory cells, and photomasks. The terms xe2x80x9cwafer diexe2x80x9d and xe2x80x9csemiconductor wafer diexe2x80x9d refer to a wafer which is divided into dies for the manufacture of semiconductor chips, such that each die becomes an individual chip, such as a memory chip or a microprocessor chip for example. The type of chip produced from each die is not relevant to the method of the present invention.